Partial task execution in a dispersed storage network

ABSTRACT

A processing system in a dispersed storage and a task network DSTN determines whether or not to execute a partial task. When determined to execute the partial task, the processing system operates by determining execution steps and a schedule; identifying a portion of the contiguous data for execution of one or more steps of the execution steps; retrieving the portion of the contiguous data from the disk drive; executing the one or more steps of the execution steps in accordance with the schedule on the portion of the contiguous data to produce a partial result; dispersed storage error encoding the partial result to produce a plurality of sets of slices in accordance with dispersal parameters associated with one or more of the group of slices and the partial task; and facilitating storing a plurality of sets of slices in the DSTN.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present U.S. Utility Patent Application claims priority pursuant to35 U.S.C. §120 as a continuation-in-part of U.S. Utility applicationSer. No. 13/865,641, entitled “DISPERSED STORAGE NETWORK SECUREHIERARCHICAL FILE DIRECTORY”, filed Apr. 18, 2013, which is acontinuation in part of U.S. Utility application Ser. No. 13/707,490,entitled “RETRIEVING DATA FROM A DISTRIBUTED STORAGE NETWORK”, filedDec. 6, 2012, issued as U.S. Pat. No. 9,304,857 on Apr. 5, 2016, whichclaims priority pursuant to 35 U.S.C. §119(e) to U.S. ProvisionalApplication No. 61/569,387, entitled “DISTRIBUTED STORAGE AND TASKPROCESSING”, filed Dec. 12, 2011, all of which are hereby incorporatedherein by reference in their entirety and made part of the present U.S.Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

DESCRIPTION OF RELATED ART

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a diagram of an example of a distributed storage and taskprocessing in accordance with the present invention;

FIG. 10 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing in accordance with thepresent invention;

FIG. 11 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 12 is a diagram of an example embodiment of a dispersed storage andtask execution unit in accordance with the present invention; and

FIG. 13 is a logic diagram of an example of a method in accordance withthe present invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12-16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

In various embodiments, each of the storage units operates as adistributed storage and task (DST) execution unit, and is operable tostore dispersed error encoded data and/or to execute, in a distributedmanner, one or more tasks on data. The tasks may be a simple function(e.g., a mathematical function, a logic function, an identify function,a find function, a search engine function, a replace function, etc.), acomplex function (e.g., compression, human and/or computer languagetranslation, text-to-voice conversion, voice-to-text conversion, etc.),multiple simple and/or complex functions, one or more algorithms, one ormore applications, etc. Hereafter, a storage unit may be interchangeablyreferred to as a DST execution unit and a set of storage units may beinterchangeably referred to as a set of DST execution units.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN memory 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (IO)controller 56, a peripheral component interconnect (PCI) interface 58,an IO interface module 60, at least one IO device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), interne small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the IO deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as IO ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. Here, the computing device stores data object40, which can include a file (e.g., text, video, audio, etc.), or otherdata arrangement. The dispersed storage error encoding parametersinclude an encoding function (e.g., information dispersal algorithm,Reed-Solomon, Cauchy Reed-Solomon, systematic encoding, non-systematicencoding, on-line codes, etc.), a data segmenting protocol (e.g., datasegment size, fixed, variable, etc.), and per data segment encodingvalues. The per data segment encoding values include a total, or pillarwidth, number (T) of encoded data slices per encoding of a data segmenti.e., in a set of encoded data slices); a decode threshold number (D) ofencoded data slices of a set of encoded data slices that are needed torecover the data segment; a read threshold number (R) of encoded dataslices to indicate a number of encoded data slices per set to be readfrom storage for decoding of the data segment; and/or a write thresholdnumber (W) to indicate a number of encoded data slices per set that mustbe accurately stored before the encoded data segment is deemed to havebeen properly stored. The dispersed storage error encoding parametersmay further include slicing information (e.g., the number of encodeddata slices that will be created for each data segment) and/or slicesecurity information (e.g., per encoded data slice encryption,compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides dataobject 40 into a plurality of fixed sized data segments (e.g., 1throughY of a fixed size in range of Kilo-bytes to Tera-bytes or more). Thenumber of data segments created is dependent of the size of the data andthe data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 78 is shown inFIG. 6. As shown, the slice name (SN) 78 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

FIG. 9 is a diagram of an example of the distributed computing systemperforming a distributed storage and task processing operation. Thedistributed computing system includes a DST (distributed storage and/ortask) client module 34 (which may be in user device 14 and/or in DSTprocessing unit 16 of FIG. 1), a network 24, a plurality of DSTexecution units 101_1 . . . 101_n that includes two or more DSTexecution units which, for example form at least a portion of DSN memory22 of FIG. 1, a DST managing module (not shown), and a DST integrityverification module (not shown). The DST client module 34 includes anoutbound DST processing section 80 and an inbound DST processing section82. Each of the DST execution units 1-n includes a controller 86, aprocessing module 84, memory 88, a DT (distributed task) executionmodule 90, and a DST client module 34.

In an example of operation, the DST client module 34 receives data 92and one or more tasks 94 to be performed upon the data 92. The data 92may be of any size and of any content, where, due to the size (e.g.,greater than a few Terra-Bytes), the content (e.g., secure data, etc.),and/or task(s) (e.g., MIPS intensive), distributed processing of thetask(s) on the data is desired. For example, the dta 92 may be one ormore digital books, a copy of a company's emails, a large-scale Internetsearch, a video security file, one or more entertainment video files(e.g., television programs, movies, etc.), data files, and/or any otherlarge amount of data (e.g., greater than a few Terra-Bytes).

Within the DST client module 34, the outbound DST processing section 80receives the data 92 and the task(s) 94. The outbound DST processingsection 80 processes the data 92 to produce slice groupings 96. As anexample of such processing, the outbound DST processing section 80partitions the data 92 into a plurality of data partitions. For eachdata partition, the outbound DST processing section 80 dispersed storage(DS) error encodes the data partition to produce encoded data slices andgroups the encoded data slices into a slice grouping 96. In addition,the outbound DST processing section 80 partitions the task 94 intopartial tasks 98, where the number of partial tasks 98 may correspond tothe number of slice groupings 96.

The outbound DST processing section 80 then sends, via the network 24,the slice groupings 96 and the partial tasks 98 to the DST executionunits 101_1 . . . 101_n of the DSN memory 22 of FIG. 1. For example, theoutbound DST processing section 80 sends slice group 96_1 and partialtask 98_1 to DST execution unit 101_1. As another example, the outboundDST processing section 80 sends slice group 96_n and partial task 98_nto DST execution unit 101_n.

Each DST execution unit performs its partial task 98 upon its slicegroup 96 to produce partial results 102. For example, DST execution unit101_1 performs partial task 98_1 on slice group 96_1 to produce apartial result 100_1. As a more specific example, slice group 96_1corresponds to a data partition of a series of digital books and thepartial task 98_1 corresponds to searching for specific phrases,recording where the phrase is found, and establishing a phrase count. Inthis more specific example, the partial result 102_1 includesinformation as to where the phrase was found and includes the phrasecount.

Upon completion of generating their respective partial results 102, theDST execution units 101 send, via the network 24, their partial results102 to the inbound DST processing section 82 of the DST client module34. The inbound DST processing section 82 processes the received partialresults 102 to produce a result 104. Continuing with the specificexample of the preceding paragraph, the inbound DST processing section82 combines the phrase count from each of the DST execution units 101_1. . . 101_n to produce a total phrase count. In addition, the inboundDST processing section 82 combines the ‘where the phrase was found’information from each of the DST execution units 101_1 . . . 101_nwithin their respective data partitions to produce ‘where the phrase wasfound’ information for the series of digital books.

In another example of operation, the DST client module 34 requestsretrieval of stored data within the memory of the DST execution units101 (e.g., memory of the DSN/DSTN). In this example, the task 94 isretrieve data stored in the memory of the DSTN. Accordingly, theoutbound DST processing section 80 converts the task 94 into a pluralityof partial tasks 98 and sends the partial tasks 98 to the respective DSTexecution units 101.

In response to the partial task 98 of retrieving stored data, a DSTexecution unit 101 identifies the corresponding encoded data slices 100and retrieves them. For example, DST execution unit 101_1 receivespartial task 98_1 and retrieves, in response thereto, retrieved slices100_1. The DST execution units 101 send their respective retrievedslices 100 to the inbound DST processing section 82 via the network 24.

The inbound DST processing section 82 converts the retrieved slices 100into data 92. For example, the inbound DST processing section 82de-groups the retrieved slices 100 to produce encoded slices per datapartition. The inbound DST processing section 82 then DS error decodesthe encoded slices per data partition to produce data partitions. Theinbound DST processing section 82 de-partitions the data partitions torecapture the data 92.

FIG. 10 is a schematic block diagram of an embodiment of an outbounddistributed storage and/or task (DST) processing section 80 of a DSTclient module 34 FIG. 1 coupled to a DSTN module 22 of a FIG. 1 (e.g., aplurality of n DST execution units 101) via a network 24. The outboundDST processing section 80 includes a data partitioning module 110, adispersed storage (DS) error encoding module 112, a grouping selectormodule 114, a control module 116, and a distributed task control module118.

In an example of operation, the data partitioning module 110 partitionsdata 92 into a plurality of data partitions 120. The number ofpartitions and the size of the partitions may be selected by the controlmodule 116 via control 160 based on the data 92 (e.g., its size, itscontent, etc.), a corresponding task 94 to be performed (e.g., simple,complex, single step, multiple steps, etc.), DS encoding parameters(e.g., pillar width, decode threshold, write threshold, segment securityparameters, slice security parameters, etc.), capabilities of the DSTexecution units 36 (e.g., processing resources, availability ofprocessing recourses, etc.), and/or as may be inputted by a user, systemadministrator, or other operator (human or automated). For example, thedata partitioning module 110 partitions the data 92 (e.g., 100Terra-Bytes) into 100,000 data segments, each being 1 Giga-Byte in size.Alternatively, the data partitioning module 110 partitions the data 92into a plurality of data segments, where some of data segments are of adifferent size, are of the same size, or a combination thereof.

The DS error encoding module 112 receives the data partitions 120 in aserial manner, a parallel manner, and/or a combination thereof. For eachdata partition 120, the DS error encoding module 112 DS error encodesthe data partition 120 in accordance with control information 160 fromthe control module 116 to produce encoded data slices 122. The DS errorencoding includes segmenting the data partition into data segments,segment security processing (e.g., encryption, compression,watermarking, integrity check (e.g., CRC), etc.), error encoding,slicing, and/or per slice security processing (e.g., encryption,compression, watermarking, integrity check (e.g., CRC), etc.). Thecontrol information 160 indicates which steps of the DS error encodingare active for a given data partition and, for active steps, indicatesthe parameters for the step. For example, the control information 160indicates that the error encoding is active and includes error encodingparameters (e.g., pillar width, decode threshold, write threshold, readthreshold, type of error encoding, etc.).

The group selecting module 114 groups the encoded slices 122 of a datapartition into a set of slice groupings 96. The number of slicegroupings corresponds to the number of DST execution units 36 identifiedfor a particular task 94. For example, if five DST execution units 101are identified for the particular task 94, the group selecting modulegroups the encoded slices 122 of a data partition into five slicegroupings 96. The group selecting module 114 outputs the slice groupings96 to the corresponding DST execution units 101 via the network 24.

The distributed task control module 118 receives the task 94 andconverts the task 94 into a set of partial tasks 98. For example, thedistributed task control module 118 receives a task to find where in thedata (e.g., a series of books) a phrase occurs and a total count of thephrase usage in the data. In this example, the distributed task controlmodule 118 replicates the task 94 for each DST execution unit 101 toproduce the partial tasks 98. In another example, the distributed taskcontrol module 118 receives a task to find where in the data a firstphrase occurs, wherein in the data a second phrase occurs, and a totalcount for each phrase usage in the data. In this example, thedistributed task control module 118 generates a first set of partialtasks 98 for finding and counting the first phase and a second set ofpartial tasks for finding and counting the second phrase. Thedistributed task control module 118 sends respective first and/or secondpartial tasks 98 to each DST execution unit 101.

FIG. 11 is a logic diagram of an example of a method for outbounddistributed storage and task (DST) processing that begins at step 126where a DST client module receives data and one or more correspondingtasks. The method continues at step 128 where the DST client moduledetermines a number of DST units to support the task for one or moredata partitions. For example, the DST client module may determine thenumber of DST units to support the task based on the size of the data,the requested task, the content of the data, a predetermined number(e.g., user indicated, system administrator determined, etc.), availableDST units, capability of the DST units, and/or any other factorregarding distributed task processing of the data. The DST client modulemay select the same DST units for each data partition, may selectdifferent DST units for the data partitions, or a combination thereof.

The method continues at step 130 where the DST client module determinesprocessing parameters of the data based on the number of DST unitsselected for distributed task processing. The processing parametersinclude data partitioning information, DS encoding parameters, and/orslice grouping information. The data partitioning information includes anumber of data partitions, size of each data partition, and/ororganization of the data partitions (e.g., number of data blocks in apartition, the size of the data blocks, and arrangement of the datablocks). The DS encoding parameters include segmenting information,segment security information, error encoding information (e.g.,dispersed storage error encoding function parameters including one ormore of pillar width, decode threshold, write threshold, read threshold,generator matrix), slicing information, and/or per slice securityinformation. The slice grouping information includes informationregarding how to arrange the encoded data slices into groups for theselected DST units. As a specific example, if the DST client moduledetermines that five DST units are needed to support the task, then itdetermines that the error encoding parameters include a pillar width offive and a decode threshold of three.

The method continues at step 132 where the DST client module determinestask partitioning information (e.g., how to partition the tasks) basedon the selected DST units and data processing parameters. The dataprocessing parameters include the processing parameters and DST unitcapability information. The DST unit capability information includes thenumber of DT (distributed task) execution units, execution capabilitiesof each DT execution unit (e.g., MIPS capabilities, processing resources(e.g., quantity and capability of microprocessors, CPUs, digital signalprocessors, co-processor, microcontrollers, arithmetic logic circuitry,and/or and the other analog and/or digital processing circuitry),availability of the processing resources, memory information (e.g.,type, size, availability, etc.)), and/or any information germane toexecuting one or more tasks.

The method continues at step 134 where the DST client module processesthe data in accordance with the processing parameters to produce slicegroupings. The method continues at step 136 where the DST client modulepartitions the task based on the task partitioning information toproduce a set of partial tasks. The method continues at step 138 wherethe DST client module sends the slice groupings and the correspondingpartial tasks to respective DST units.

FIG. 12 is a diagram of an example embodiment of a dispersed storage andtask execution unit 101 that includes an interface 169, a computing core26, a controller 86, at least one memory 88, and one or more memorymodules 350. A memory module 350 of the one or more memory modules 350may include a memory device 352 (e.g., implemented utilizing FLASHmemory technology, a random access memory, a read-only memory, amagnetic disk drive, and an optical disk drive), may include one or moredistributed task (DT) execution modules 90 (e.g., implemented utilizingat least one of a processing module, and a computing core), and mayinclude one or more DST client module 34. For example, a memory device352 is implemented by adding a processing core (e.g., to enable a DTexecution module) to a FLASH memory. As another example, a memory device352 is implemented by adding four processing cores to the FLASH memory.Alternatively, or in addition to, the memory device 352 includes one ormore distributed storage and task (DST) client modules 34. As yetanother example, a memory module 350 is implemented as a disk drive unitthat includes one DT execution module 90 and four memory devices 352(e.g. disk drives). As a still further example, a memory module 350 isimplemented as a disk drive unit that includes 100 DT execution modules90 and 10 memory devices 352 (e.g. disk drives).

The operation of the DST execution unit 101 can be further illustratedin conjunction with the following example. A processing module (e.g.computing core 26 or other processing device of the DST execution unit101) receives at least one partial task 98_1 . . . 98_n with regards toa group of slices 96_1 . . . 96_n of contiguous data (e.g., from a DSTclient module 34). The processing module receives slices of the group ofslices to produce received slices. When an interim threshold number(e.g., a maximum number of bytes limited by an ingestion cache memory)of received slices has been received, the processing module streams thereceived slices to a disk drive for storage therein. The streaming mayprovide a write bandwidth system improvement for the group of slices(e.g., as the group of slices pertain to the contiguous data).

The processing module determines whether to execute a partial task. Thedetermining may be based on one or more of comparing an amount of datareceived to a data threshold, a partial task type, task executionresource availability, and a task schedule. For example, the processingmodule determines to execute the partial task when data of the receivedslices can be processed in accordance with a partial task. When theprocessing module determines not to execute the partial task, theprocessing module determines whether more slices are expected. Thedetermining may be based on one or more of a contiguous data sizeindicator, a query, a lookup, and a number of bytes received so far. Theprocessing module repeats back to receive slices of the group of slicesto produce received slices, when the processing module determines thatthere are more slices. When either the processing module determines thatthere are no more slices, or determines to execute a partial task, theprocessing module determines execution steps and schedule. Thedetermining may be based on one or more of the at least one partialtask, the data, a previous task schedule, a schedule template, a taskexecution resource availability level, and a task execution requirement.The processing module identifies a portion of the contiguous data forexecution of one or more steps of the execution steps. The identifyingincludes matching the portion of the contiguous data to the one or moresteps of execution steps based on one or more of a data type indicatorassociated with the portion, a data type associated with one or moresteps, and a data available indicator.

The processing module retrieves the portion of the contiguous data fromthe disk drive as a data stream. The retrieving includes accessing thedisk drive for multiple contiguous data bytes. The streaming may providea read bandwidth system improvement for the portion of data. Theprocessing module executes the steps in accordance with the schedule onthe portion of the contiguous data to produce a partial result. Forexample, the processing module executes a search partial task on theportion to produce a search partial result.

The processing module dispersed storage error encodes the partialresults to produce a plurality of sets of slices in accordance withdispersal parameters associated with one or more of the group of slicesand the at least one partial task. The processing module facilitatesstoring a plurality of sets of slices in a dispersed storage and tasknetwork (DSTN). For example, the processing module sends groups ofslices to a DST EX unit, where the slices are of a common pillar numberwhen a storage method indicates dispersed storage. As another example,the processing module sends groups of slices to a DST EX unit, where theslices are of two or more pillar number when a storage method indicatesdistributed task storage to enable subsequent task execution on thepartial result. In addition, the processing module may receive moreslices for more execution steps.

FIG. 13 is a flowchart illustrating an example of storing and processinga group of slices. The method begins at step 354 where a processingmodule (e.g., of a distributed task (DT) execution module of adistributed storage and task execution (DST EX) unit embedded within adisk drive unit) receives at least one partial task with regards to agroup of slices of contiguous data (e.g., from a DST client module). Themethod continues at step 356 where the processing module receives slicesof the group of slices to produce received slices. The method continuesat step 358 where, when an interim threshold number (e.g., a maximumnumber of bytes limited by an ingestion cache memory) of received sliceshas been received, the processing module streams the received slices toa disk drive for storage therein. The streaming may provide a writebandwidth system improvement for the group of slices (e.g., as the groupof slices pertain to the contiguous data).

The method continues at step 360 where the processing module determineswhether to execute a partial task. The determining may be based on oneor more of comparing an amount of data received to a data threshold, apartial task type, task execution resource availability, and a taskschedule. For example, the processing module determines to execute thepartial task when data of the received slices can be processed inaccordance with a partial task. The method branches to step 364 when theprocessing module determines to execute the partial task. The methodcontinues to step 362 when the processing module determines not toexecute the partial task.

The method continues at step 362 where the processing module determineswhether more slices are expected. The determining may be based on one ormore of a contiguous data size indicator, a query, a lookup, and anumber of bytes received so far. The method repeats back to step 356when the processing module determines that there are more slices. Themethod continues to step 364 when the processing module determines thatthere are no more slices.

The method continues at step 364 where the processing module determinesexecution steps and schedule. The determining may be based on one ormore of the at least one partial task, the data, a previous taskschedule, a schedule template, a task execution resource availabilitylevel, and a task execution requirement. The method continues at step366 where the processing module identifies a portion of the contiguousdata for execution of one or more steps of the execution steps. Theidentifying includes matching the portion of the contiguous data to theone or more steps of execution steps based on one or more of a data typeindicator associated with the portion, a data type associated with oneor more steps, and a data available indicator.

The method continues at step 368 where the processing module retrievesthe portion of the contiguous data from the disk drive as a data stream.The retrieving includes accessing the disk drive for multiple contiguousdata bytes. The streaming may provide a read bandwidth systemimprovement for the portion of data. The method continues at step 370where the processing module executes the steps in accordance with theschedule on the portion of the contiguous data to produce a partialresult. For example, the processing module executes a search partialtask on the portion to produce a search partial result.

The method continues at step 372 where the processing module dispersedstorage error encodes the partial results to produce a plurality of setsof slices in accordance with dispersal parameters associated with one ormore of the group of slices and the at least one partial task. Themethod continues at step 374 where the processing module facilitatesstoring a plurality of sets of slices in a dispersed storage and tasknetwork (DSTN). For example, the processing module sends groups ofslices to a DST EX unit, where the slices are of a common pillar numberwhen a storage method indicates dispersed storage. As another example,the processing module sends groups of slices to a DST EX unit, where theslices are of two or more pillar number when a storage method indicatesdistributed task storage to enable subsequent task execution on thepartial result. In addition, the processing module may receive moreslices for more execution steps.

In various embodiments, a non-transitory computer readable storagemedium includes at least one memory section that stores operationalinstructions that, when executed by a processing system of a dispersedstorage network (DSN) that includes a processor and a memory, causes theprocessing system to perform operations including: receiving a partialtask with regards to a group of slices of contiguous data; receivingslices of the group of slices to produce received slices; determiningwhen a threshold number of the received slices has been received; whenthe threshold number of the received slices has been received, streamingthe received slices to a disk drive for storage; determining whether ornot to execute the partial task; when determined to execute the partialtask: determining execution steps and a schedule; identifying a portionof the contiguous data for execution of one or more steps of theexecution steps; retrieving the portion of the contiguous data from thedisk drive; executing the one or more steps of the execution steps inaccordance with the schedule on the portion of the contiguous data toproduce a partial result; dispersed storage error encoding the partialresult to produce a plurality of sets of slices in accordance withdispersal parameters associated with one or more of the group of slicesand the partial task; and facilitating storing a plurality of sets ofslices in a dispersed storage and task network (DSTN).

In various embodiments, the threshold number corresponds to a maximumnumber of bytes limited by an ingestion cache memory. Determiningwhether or not to execute the partial task can be based on one or moreof: comparing an amount of data in the received slices to a datathreshold, a partial task type corresponding to the partial task, a taskexecution resource availability or a task schedule and/or can includedetermining to execute the partial task when data of the received slicescan be processed in accordance with the partial task. Retrieving theportion of the contiguous data from the disk drive can include receivinga data stream from the disk drive that contains the portion of thecontiguous data.

In various embodiments, the method further includes, when the determinednot to execute the partial task: determining whether or not more slicesof the group of slices are expected; and when more slices are expected,receiving the more slices of the group of slices to produce morereceived slices. Determining whether or not more slices of the group ofslices are expected can be based on one or more of: a contiguous datasize indicator, a query, a lookup, or a number of bytes in the receivedslices.

In various embodiments, determining the execution steps and the schedulecan be based on one or more of: the partial task, the contiguous data, aprevious task schedule, a schedule template, a task execution resourceavailability level or a task execution requirement. Identifying theportion of the contiguous data can include matching the portion of thecontiguous data to the one or more steps of execution steps based on oneor more of: a data type indicator associated with the portion, a datatype associated with one or more steps or a data available indicator.Retrieving the portion of the contiguous data from the disk drive caninclude accessing the disk drive for multiple contiguous data bytes ofthe portion of the contiguous data. Facilitating the storing of theplurality of sets of slices can includes storing of the plurality ofsets of slices with a common pillar number or with two or more pillarnumbers.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the Figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A method for execution by a processing system ofa dispersed storage and task (DST) execution unit that includes aprocessor, the method comprises: receiving a partial task with regardsto a group of slices of contiguous data; receiving slices of the groupof slices to produce received slices; determining when a thresholdnumber of the received slices has been received; when the thresholdnumber of the received slices has been received, streaming the receivedslices to a disk drive for storage; determining whether or not toexecute the partial task; when determined to execute the partial task:determining execution steps and a schedule; identifying a portion of thecontiguous data for execution of one or more steps of the executionsteps; retrieving the portion of the contiguous data from the diskdrive; executing the one or more steps of the execution steps inaccordance with the schedule on the portion of the contiguous data toproduce a partial result; dispersed storage error encoding the partialresult to produce a plurality of sets of slices in accordance withdispersal parameters associated with one or more of the group of slicesand the partial task; and facilitating storing a plurality of sets ofslices in a dispersed storage and task network (DSTN).
 2. The method ofclaim 1, wherein the threshold number corresponds to a maximum number ofbytes limited by an ingestion cache memory.
 3. The method of claim 1,wherein determining whether or not to execute the partial task is basedon one or more of: comparing an amount of data in the received slices toa data threshold, a partial task type corresponding to the partial task,a task execution resource availability or a task schedule.
 4. The methodof claim 1, wherein determining whether or not to execute the partialincludes determining to execute the partial task when data of thereceived slices can be processed in accordance with the partial task. 5.The method of claim 1, wherein retrieving the portion of the contiguousdata from the disk drive includes receiving a data stream from the diskdrive that contains the portion of the contiguous data.
 6. The method ofclaim 1, further comprising: when the determined not to execute thepartial task: determining whether or not more slices of the group ofslices are expected; when more slices are expected, receiving the moreslices of the group of slices to produce more received slices.
 7. Themethod of claim 6, wherein determining whether or not more slices of thegroup of slices are expected is based on one or more of: a contiguousdata size indicator, a query, a lookup, or a number of bytes in thereceived slices.
 8. The method of claim 1, wherein determining theexecution steps and the schedule is based on one or more of: the partialtask, the contiguous data, a previous task schedule, a scheduletemplate, a task execution resource availability level or a taskexecution requirement.
 9. The method of claim 1, wherein identifying theportion of the contiguous data includes matching the portion of thecontiguous data to the one or more steps of execution steps based on oneor more of: a data type indicator associated with the portion, a datatype associated with one or more steps or a data available indicator.10. The method of claim 1, wherein retrieving the portion of thecontiguous data from the disk drive includes accessing the disk drivefor multiple contiguous data bytes of the portion of the contiguousdata.
 11. The method of claim 1, wherein facilitating the storing of theplurality of sets of slices includes storing of the plurality of sets ofslices with a common pillar number.
 12. The method of claim 1, whereinfacilitating the storing of the plurality of sets of slices includesstoring of the plurality of sets of slices with two or more pillarnumbers.
 13. A processing system of a dispersed storage and task (DST)execution unit comprises: at least one processor; a memory that storesoperational instructions, that when executed by the at least oneprocessor cause the processing system to perform operations including:receiving a partial task with regards to a group of slices of contiguousdata; receiving slices of the group of slices to produce receivedslices; determining when a threshold number of the received slices hasbeen received; when the threshold number of the received slices has beenreceived, streaming the received slices to a disk drive for storage;determining whether or not to execute the partial task; when determinedto execute the partial task: determining execution steps and a schedule;identifying a portion of the contiguous data for execution of one ormore steps of the execution steps; retrieving the portion of thecontiguous data from the disk drive; executing the one or more steps ofthe execution steps in accordance with the schedule on the portion ofthe contiguous data to produce a partial result; dispersed storage errorencoding the partial result to produce a plurality of sets of slices inaccordance with dispersal parameters associated with one or more of thegroup of slices and the partial task; and facilitating storing aplurality of sets of slices in a dispersed storage and task network(DSTN).
 14. The processing system of claim 13, wherein the thresholdnumber corresponds to a maximum number of bytes limited by an ingestioncache memory.
 15. The processing system of claim 13, wherein determiningwhether or not to execute the partial task is based on one or more of:comparing an amount of data in the received slices to a data threshold,a partial task type corresponding to the partial task, a task executionresource availability or a task schedule.
 16. The processing system ofclaim 13, wherein determining whether or not to execute the partialincludes determining to execute the partial task when data of thereceived slices can be processed in accordance with the partial task.17. The processing system of claim 13, wherein retrieving the portion ofthe contiguous data from the disk drive includes receiving a data streamfrom the disk drive that contains the portion of the contiguous data.18. The processing system of claim 13, further comprising: when thedetermined not to execute the partial task: determining whether or notmore slices of the group of slices are expected; when more slices areexpected, receiving the more slices of the group of slices to producemore received slices.
 19. The processing system of claim 13, whereindetermining the execution steps and the schedule is based on one or moreof: the partial task, the contiguous data, a previous task schedule, aschedule template, a task execution resource availability level or atask execution requirement.
 20. A non-transitory computer readablestorage medium comprises: at least one memory section that storesoperational instructions that, when executed by a processing system of adispersed storage network (DSN) that includes a processor and a memory,causes the processing system to perform operations including: receivinga partial task with regards to a group of slices of contiguous data;receiving slices of the group of slices to produce received slices;determining when a threshold number of the received slices has beenreceived; when the threshold number of the received slices has beenreceived, streaming the received slices to a disk drive for storage;determining whether or not to execute the partial task; when determinedto execute the partial task: determining execution steps and a schedule;identifying a portion of the contiguous data for execution of one ormore steps of the execution steps; retrieving the portion of thecontiguous data from the disk drive; executing the one or more steps ofthe execution steps in accordance with the schedule on the portion ofthe contiguous data to produce a partial result; dispersed storage errorencoding the partial result to produce a plurality of sets of slices inaccordance with dispersal parameters associated with one or more of thegroup of slices and the partial task; and facilitating storing aplurality of sets of slices in a dispersed storage and task network(DSTN).